Quantifying, tracking, and anticipating risk at a manufacturing facility based on staffing conditions and textual descriptions of deviations

ABSTRACT

A system comprising a computer-readable storage medium storing at least one program and a method for determining, tracking, and anticipating risk in a manufacturing facility are presented. In example embodiments, the method includes generating a risk data model for the manufacturing facility based on correlations between historical staffing conditions of the manufacturing facility and deviations from existing manufacturing procedures. The method further includes receiving projected operational data that includes information related to anticipated future staffing conditions of the manufacturing facility. The method further includes calculating a risk score based on the projected operational data using the risk data model. The method further includes causing presentation of a user interface that includes a display of the risk score.

PRIORITY CLAIM

This application is a continuation of and claims the benefit of U.S.Pat. No. 9,671,776, filed on Aug. 20, 2015, entitled “QUANTIFYING,TRACKING, AND ANTICIPATING RISK AT A MANUFACTURING FACILITY,” which isincorporated herein by reference in their entirety.

TECHNICAL FIELD

The subject matter disclosed herein relates to data processing. Inparticular, example embodiments may relate to techniques forquantifying, tracking, and anticipating risk at a manufacturingfacility.

BACKGROUND

Product manufacturing, especially pharmaceutical drug manufacturingnecessarily entails some amount of risk. That is to say that in manymanufacturing procedures that may be used in making products, therealways exists some probability that an issue may occur. In someinstances, deviations in an established manufacturing procedure may leadto unrecoverable product losses, and thus, unrecoverable profit loss.Even worse, if undetected, a deviation in the manufacturing process of aproduct has potential to cause injury to an end consumer of the product.

It is therefore important for manufacturing facility stakeholders to beable to understand what aspects of their manufacturing facility'soperations contribute to risk so that corrective action can be taken toavoid or mitigate such risk in the future. However, achieving a commonand accurate understanding of the risk is difficult because individualstakeholders may recognize different risk factors, and may attributedifferent severities to each factor. Further, heuristic methods used intraditional industry practice, which are devoid of data-driven metricsand analysis, often fail to identify correct risk factors and furtherfail to accurately determine a probability that such factors willactually lead to an issue occurring.

BRIEF DESCRIPTION OF THE DRAWINGS

Various ones of the appended drawings merely illustrate exampleembodiments of the present inventive subject matter and cannot beconsidered as limiting its scope.

FIG. 1 is an architecture diagram depicting a data processing platformhaving a client-server architecture configured for exchanging data,according to an example embodiment.

FIG. 2 is a block diagram illustrating various modules comprising amanufacturing risk analytics service, which is provided as part of thedata processing platform, consistent with some embodiments.

FIG. 3 is a flowchart illustrating a method for generating a risk datamodel associated with a manufacturing facility, consistent with someembodiments.

FIG. 4 is a block diagram illustrating a risk data model, consistentwith some embodiments.

FIG. 5 is a flowchart illustrating a method for determining riskassociated with a manufacturing facility, according to some embodiments.

FIG. 6 is a flowchart illustrating a method for calculating a risk scoreassociated with a manufacturing facility, according to some embodiments.

FIG. 7 is an interface diagram illustrating a user interface forproviding an overview of risk associated with a manufacturing facility,according to example embodiments.

FIG. 8 is an interface diagram illustrating a user interface forproviding an overview of risk associated with a manufacturing facility,according to alternative example embodiments.

FIG. 9 is an interface diagram illustrating a user interface forproviding an overview of deviations occurring at a manufacturingfacility, according to example embodiments.

FIG. 10 is an interface diagram illustrating a user interface forproviding a detailed breakdown of deviations occurring at amanufacturing facility, according to example embodiments.

FIG. 11 is an interface diagram illustrating a user interface forpresenting information related to chronic deviations in a manufacturingfacility, according to example embodiments.

FIG. 12 is an interface diagram illustrating a user interface forpresenting deviation timelines, according to example embodiments.

FIG. 13 is an interface diagram illustrating a user interface forpresenting risk analytics related to staffing conditions, according toexample embodiments.

FIG. 14 is an interface diagram illustrating a user interface forpresenting risk analytics associated with future projections, accordingto example embodiments.

FIG. 15 is an interface diagram illustrating a user interface forpresenting an overview of tasks associated with a manufacturingfacility, according to example embodiments.

FIG. 16 is an interface diagram illustrating a user interface forpresenting a detailed breakdown of tasks associated with a manufacturingfacility, according to example embodiments.

FIG. 17 is a diagrammatic representation of a machine in the exampleform of a computer system within which a set of instructions for causingthe machine to perform any one or more of the methodologies discussedherein may be executed.

DETAILED DESCRIPTION

Reference will now be made in detail to specific example embodiments forcarrying out the inventive subject matter. Examples of these specificembodiments are illustrated in the accompanying drawings, and specificdetails are set forth in the following description in order to provide athorough understanding of the subject matter. It will be understood thatthese examples are not intended to limit the scope of the claims to theillustrated embodiments. On the contrary, they are intended to coversuch alternatives, modifications, and equivalents as may be includedwithin the scope of the disclosure.

Aspects of the present disclosure relate to systems and methods forquantifying, tracking, and anticipating risk in a manufacturingfacility. Example embodiments involve systems and methods for generatingvarious risk analytics associated with the manufacturing facility. Therisk analytics are generated by analyzing data related to the operationsof the manufacturing facility. For example, the method may includeanalyzing deviation reports that describe deviations from existingmanufacturing procedures (referred to hereinafter simply as“deviations”). These deviations provide a quantifiable representation ofthe risk associated with the manufacturing facility. In exampleembodiments, the deviation reports are analyzed to provide an overviewof the deviations that occur over time as well as a breakdown ofdeviations by type, subtype, root cause, work center, line-operations,product, and criticality, for example. Further, classification logic maybe employed in the analysis of deviations reports to classify deviationsinto groups to enable the identification of repeat deviations.

Additional example embodiments involve systems and methods to generate arisk data model for the manufacturing facility. The risk data model maybe used to determine the level of risk associated with the manufacturingfacility. Accordingly, the risk data model includes factors thatcontribute to the risk in the manufacturing facility and indicators ofthe relative contribution each factor makes to the overall risk. Therisk data model may be developed through an analysis of deviations thathave occurred in the manufacturing facility and staffing conditionsduring such deviations. More particularly, at least some of the riskfactors included in the risk data model are based on correlationsbetween the deviations and the staffing conditions. Additional riskfactors included in the risk data model may, for example, relate tofinancial risks (e.g., risk of financial issues affecting themanufacturing site), manufacturing risk (e.g., risk of manufacturingproblems such as those caused by legacy systems), risks associated withexcipients (e.g., risk of input materials and chemicals causing aproblem), risks associated with change control (e.g., risk associatedwith change control events such as new processes or procedures, orfailure to adhere to existing ones), and risk associated with mothernature (e.g., lightning storms, high humidity, high or low temperatures,or the like).

Additional example embodiments involve systems and methods to determinea risk level associated with the manufacturing facility. Thedetermination of the risk level includes calculating a risk score forthe manufacturing facility using a risk data model and informationrelated to current or projected staffing conditions of the manufacturingfacility. The risk score is then used to determine the overall risklevel of the facility as well as risk levels associated with individualrisk factors. Risk analytics, risk scores, and risk levels are presentedto users such as stakeholders of the manufacturing facility so as toassist those stakeholders in making informed decisions about actions totake to avoid, or at least mitigate, further risk.

FIG. 1 is an architecture diagram depicting a network system 100 havinga client-server architecture configured for exchanging data, accordingto an example embodiment. While the network system 100 shown in FIG. 1employs a client-server architecture, the present inventive subjectmatter is, of course, not limited to such an architecture, and couldequally well find application in an event-driven, distributed, orpeer-to-peer architecture system, for example. Moreover, it shall beappreciated that although the various functional components of thenetwork system 100 are discussed in the singular sense, multipleinstances of one or more of the various functional components may beemployed.

As shown, the network system 100 includes a client device 102 incommunication with a data processing platform 104 over a network 106.The data processing platform 104 communicates and exchanges data withthe client device 102 that pertains to various functions and aspectsassociated with the network system 100 and its users. Likewise, theclient device 102, which may be any of a variety of types of devicesthat include at least a display, a processor, and communicationcapabilities that provide access to the network 106 (e.g., a smartphone, a tablet computer, a personal digital assistant (PDA), a personalnavigation device (PND), a handheld computer, a desktop computer, alaptop or netbook, or a wearable computing device), may be operated by auser (e.g., a person) of the network system 100 to exchange data withthe data processing platform 104 over the network 106.

The client device 102 communicates with the network 106 via a wired orwireless connection. For example, one or more portions of the network106 may comprises an ad hoc network, an intranet, an extranet, a VirtualPrivate Network (VPN), a Local Area Network (LAN), a wireless LAN(WLAN), a Wide Area Network (WAN), a wireless WAN (WWAN), a MetropolitanArea Network (MAN), a portion of the Internet, a portion of the PublicSwitched Telephone Network (PSTN), a cellular telephone network, awireless network, a Wireless Fidelity (Wi-Fi®) network, a WorldwideInteroperability for Microwave Access (WiMax) network, another type ofnetwork, or any suitable combination thereof.

In various embodiments, the data exchanged between the client device 102and the data processing platform 104 may involve user-selected functionsavailable through one or more user interfaces (UIs). The UIs may bespecifically associated with a web client 108 (e.g., a browser) or anapplication 109, executing on the client device 102, and incommunication with the data processing platform 104.

Turning specifically to the data processing platform 104, a web server110 is coupled to (e.g., via wired or wireless interfaces), and providesweb interfaces to, an application server 112. The application server 112hosts one or more applications (e.g., web applications) that allow usersto use various functions and services of the data processing platform104. For example, the application server 112 may host a manufacturingrisk analytics service 114 that provides a number of analytics relatedto risk associated with product manufacturing (e.g., pharmaceutical drugmanufacturing). In some embodiments, the manufacturing risk analyticsservice 114 runs and executes on the application server 112, while inother embodiments, the application server 112 provides the client device102 with a set of instructions (e.g., computer-readable code) thatcauses the web client 108 of the client device 102 to execute and runthe manufacturing risk analytics service 114. The manufacturing riskanalytics service 114 analyzes various data to quantify the riskassociated with a manufacturing facility and to provide stakeholderswith a number of risk analytics to assist the stakeholders in trackingrisk at their facilities. Further, the manufacturing risk analyticsservice 114 makes use of the determined risk analytics to assiststakeholders in anticipating risk based on projected changes inemployment and manufacturing output.

The data analyzed by the manufacturing risk analytics service 114 may,for example, include operational data and deviation report data. Thedeviation report data includes deviation reports that describe adeviation from an existing manufacturing procedure (e.g., an approvedinstruction or an established standard). Each deviation report mayinclude a timestamp (e.g., a time and date of the deviation), a textualdescription of the deviation, and a criticality category (e.g.,incident, minor deviation, major deviation, critical deviation).

The operational data includes information related to staffing conditionsof the manufacturing facility. For example, the operational data mayinclude a total number of employees of the manufacturing facility, atotal number of employees of the manufacturing facility who aretemporary employees, a total cost to employ the employees of themanufacturing facility, a total number of hours worked by the employeesof the manufacturing facility, and a total number of overtime hoursworked by the employees of the manufacturing facility. The operationaldata also includes output data including an amount of goods produced bythe employees of the manufacturing facility (e.g., represented as adollar amount). The operational data also includes information relatedto customer complaints associated with the manufacturing facility,scheduled audits, previously performed audits, and task data includingoverdue, scheduled, and upcoming tasks. The operational data analyzed bythe manufacturing risk analytics service 114 includes historicaloperational data (e.g., information related to staffing conditions overa previous period of time), current operational data (e.g., informationrelated to current staffing conditions), and projected operational data(e.g., information related to anticipated staffing conditions).

The data analyzed by the manufacturing risk analytics service 114 isobtained from a third-party computing system 118 (e.g., corresponding toa manufacturing facility), and in particular, a third-party database 120communicatively coupled to the third-party computing system 118. Thedata may be routinely automatically retrieved (e.g., nightly) by themanufacturing risk analytics service 114, or manually provided by a userof the third-party computing system 118 or the client device 102 forsubsequent processing and analysis by the manufacturing risk analyticsservice 114.

The data obtained from the third-party computing system 118 is stored ina database 116 that is communicatively coupled to the application server112 (e.g., via wired or wireless interfaces). The data processingplatform 104 may further include a database server (not shown) thatfacilitates access to the database 116. The database 116 may includemultiple databases that may be internal or external to the dataprocessing platform 104. Data representative of the various riskanalytics and other pertinent data generated by the manufacturing riskanalytics service 114 are also stored on the database 116.

FIG. 2 is a block diagram illustrating various modules comprising themanufacturing risk analytics service 114, which is provided as part ofthe data processing platform 104, consistent with some embodiments. Asis understood by skilled artisans in the relevant computer andInternet-related arts, the modules and engines illustrated in FIG. 2represent a set of executable software instructions and thecorresponding hardware (e.g., memory and processor) for executing theinstructions. To avoid obscuring the inventive subject matter withunnecessary detail, various functional components (e.g., modules andengines) that are not germane to conveying an understanding of theinventive subject matter have been omitted from FIG. 2. However, askilled artisan will readily recognize that various additionalfunctional components may be supported by the manufacturing riskanalytics service 114 to facilitate additional functionality that is notspecifically described herein. Furthermore, the various functionalmodules and engines depicted in FIG. 2 may reside on a single computer(e.g., a client device), or may be distributed across several computersin various arrangements such as cloud-based architectures.

The manufacturing risk analytics service 114 is shown as including aninterface module 200, a data retrieval module 205, a data analysismodule 210, a classifier module 215, a risk modeling engine 220, and arisk scoring module 225, all configured to communicate with each other(e.g., via a bus, shared memory, a switch, or application programminginterfaces (APIs)). The aforementioned modules of the manufacturing riskanalytics service 114 may, furthermore, access one or more databasesthat are part of the data processing platform 104 (e.g., database 116),and each of the modules may access one or more computer-readable storagemedia of the client device 102.

The interface module 200 receives requests from various client computingdevices, and communicates appropriate responses to the requesting clientdevices. The interface module 200 may receive requests from clientdevices in the form of Hypertext Transfer Protocol (HTTP) requests, orother web-based, application programming interface (API) requests. Forexample, the interface module 200 provides a number of interfaces (e.g.,APIs or user interfaces that are presented by the client device 102)that allow data to be received by the manufacturing risk analyticsservice 114.

The interface module 200 also provides user interfaces that includegraphical representations of the various analytics produced by themanufacturing risk analytics service 114. These user interfaces mayinclude various graphs, charts, and other graphics used, for example, torepresent and analyze risk associated with a manufacturing facility or aset of manufacturing facilities. The interface module 200 also receivesand processes user input received through such user interfaces. Examplesof the user interfaces provided by the interface module 200 arediscussed below in reference to FIGS. 7-16.

The data retrieval module 205 is configured to retrieve data forprocessing and analysis. For example, the data retrieval module 205obtains deviation report data and operational data associated withmanufacturing facilities. In some embodiments, the data retrieval module205 retrieves such data from the third-party database 120 of thethird-party computing system 118 through appropriate requests (e.g., APIrequests or calls) transmitted over the network 106. The data may beretrieved by the data retrieval module 205 on a periodic basis (e.g.,nightly). In some embodiments, the data retrieval module 205 obtainsdata from a location specified by a user (e.g., via a user interfaceprovided by the interface module 200).

The data analysis module 210 is configured to analyze data to developvarious analytics related to risk in a manufacturing facility. Forexample, the data analysis module 210 analyzes deviation report data toidentify patterns in occurrences of deviations (e.g., departures from anexisting manufacturing procedure). Given a set of deviation report data,the data analysis module 210 determines a number of analytics includinga breakdown of the number of deviations that occur by type, subtype,root cause, work center, line-operations, product, and criticality, forexample.

The data analysis module 210 also tracks the occurrences of deviationsover time to identify chronic deviations. For example, the data analysismodule 210 analyzes deviation report data to identify repeat deviations,which are multiple occurrences of the same deviation occurring within apredefined time range of one another. The data analysis module 210includes classification logic that is used to classify deviations intogroups so as to enable the data analysis module 210 to identify repeatdeviations.

In some instances, the workload of employees at a manufacturing facility(e.g., a number of tasks assigned to each employee) contributes to theamount of risk at the manufacturing facility. Accordingly, the dataanalysis module 210 also analyzes task data (e.g., included as part ofthe operational data) obtained from a manufacturing facility to developanalytics to assist manufacturing facility stakeholders with taskmanagement and workload balancing. In some embodiments, the analysis ofthe task data may also be used in the determination of the amount ofrisk associated with manufacturing facilities.

The task data includes information related to planned and upcoming tasksassociated with the manufacturing facility. The task data may include ascheduled date or date range, a due date, a responsible party (e.g., anemployee of the manufacturing facility to whom the task is assigned),and a description of the task. The data analysis module 210 analyzes thetask data to generate an overview of the tasks performed over a certaintime period, and determine a number of overdue tasks, an amount of timeeach task is overdue, and a number of tasks scheduled for a given timeperiod. The data analysis module 210 also generates a breakdown of tasksdue by days overdue, due date, status, and criticality. The dataanalysis module 210 also generates a breakdown of the tasks assigned toeach employee of the manufacturing facility and the status of each task(e.g., overdue, scheduled, and upcoming).

The classifier module 215 is configured to analyze the textualdescriptions of deviations included in deviation report data to classifyeach deviation into one or more of several deviation types and subtypes.The deviation types and subtypes assigned to each deviation may bestored in a record associated with the corresponding deviation. In thismanner, the data analysis module 210 can use the determined type andsubtype to produce more informed analytics (e.g., a breakdown ofdeviations by type and subtype).

The risk modeling engine 220 is configured to generate risk data modelsfor manufacturing facilities. A risk data model is used to quantify riskassociated with the manufacturing facility. The risk data model includesone or more risk metrics, which are factors that contribute to risk. Theone or more risk metrics may, for example, correspond to correlationsbetween occurrences of deviations in a manufacturing facility andstaffing conditions of the manufacturing facility. Accordingly, ingenerating the risk data model, the risk modeling engine 220 analyzesdeviation report data and historical operational data to identifycorrelations between occurrences of deviations and staffing conditions,which are used as the risk metrics in the risk data model. For example,the risk metrics may include a headcount change (e.g., a number ofemployees hired over a period of time), a percentage of hours worked byemployees that are overtime hours, a percentage of the total workforcewho are temporary employees, and productivity (e.g., amount of goodscreated relative to an amount of people creating the goods). The riskdata model also includes a weight of each risk metric to indicate anamount of risk attributable to that risk metric.

In some embodiments, the risk data model generated by the risk modelingengine 220 takes into account the output of (e.g., amount of goodsproduced by) the manufacturing facility. To this end, the risk modelingengine 220 analyzes deviation report data along with operational data toidentify trends in occurrences of deviations relative to the output ofthe manufacturing facility. In this manner, the risk data model may beused to identify risk associated with an anticipated output of themanufacturing facility.

The risk scoring module 225 generates a risk score for a manufacturingfacility using a risk data model generated by the risk modeling engine220. The risk score provides a quantified measure of risk associatedwith the manufacturing facility. Specifically, in some instances, therisk score provides an indication of the likelihood that a futuredeviation will occur in the manufacturing facility.

In generating the risk score for a manufacturing facility, the riskscoring module 225 accesses a risk data model corresponding to themanufacturing facility, and operational data associated with themanufacturing facility. The operational data may include either currentoperational data (e.g., information related to current staffingconditions of the manufacturing facility) or projected operational data(e.g., information related to anticipated staffing conditions of themanufacturing facility). In instances in which the risk score is basedon current operational data, the risk score provides an indication ofthe current risk associated with the manufacturing facility. Ininstances in which the risk score is based on projected operationaldata, the risk score provides an indication of the risk associated withthe anticipated staffing conditions.

Once the risk scoring module 225 has accessed the risk data model andthe operational data, the risk scoring module 225 determines a value foreach risk metric (referred to herein as “risk metric values”) includedin the risk data model. The risk scoring module 225 determines the riskmetric values based on the operational data. Following the example fromabove, the risk scoring module 225 determines values corresponding toheadcount change, overtime hour percentage, temporary employeepercentage, and productivity. The risk scoring module 225 then weightseach determined risk metric value based on the respective risk metricweight indicated in the risk data model. The weighted risk metric valuesare used to provide an indication of the risk associated with eachindividual risk metric; accordingly, the risk scoring module 225 maywork in conjunction with the interface module 200 to provide a displayof the weighted risk metric values. To generate the overall risk scoreassociated with the manufacturing facility, the risk scoring module 225aggregates the weighted risk metric values. For example, the riskscoring module 225 calculates an average of the weighted risk metricvalues to generate the overall risk score.

The risk scoring module 225 also determines risk levels associated withmanufacturing facilities based on the determined risk score. The risklevel may, for example, be high, medium, or low. Each risk levelcorresponds to a range of risk scores, and accordingly, thedetermination of the risk level of a manufacturing facility includesdetermining which range of values the determined risk score falls into.

In some embodiments, the risk scoring module 225 also calculates a riskscore specifically associated with a production target of themanufacturing facility. The production target includes a recovery amount(e.g., an anticipated amount of goods to be produced or output).Production target data representative of the production target may beincluded in the operational data associated with the manufacturingfacility, or provided by a user through an interface provided by theinterface module 200. The risk scoring module 225 uses the risk datamodel along with operational data associated with the manufacturingfacility to calculate the risk level associated with the productiontarget.

FIG. 3 is a flowchart illustrating a method 300 for generating a riskdata model for a manufacturing facility, consistent with someembodiments. The method 300 may be embodied in computer-readableinstructions for execution by one or more processors such that theoperations of the method 300 may be performed in part or in whole by theapplication server 112. In particular, the operations of the method 300may be performed in part or in whole by the manufacturing risk analyticsservice 114; accordingly, the method 300 is described below by way ofexample with reference thereto. However, it shall be appreciated that atleast some of the operations of the method 300 may be deployed onvarious other hardware configurations and the method 300 is not intendedto be limited to the application server 112 or the manufacturing riskanalytics service 114.

At operation 305, the risk modeling engine 220 obtains deviation reportdata associated with a manufacturing facility. The deviation report dataincludes one or more deviation reports identifying a deviation from amanufacturing procedure (e.g., an established standard or approvedinstruction). Each deviation report includes a timestamp, a criticalitycategory, and a textual description of the deviation. An example of adeviation is placing an incorrect label on a product.

At operation 310, the risk modeling engine 220 obtains historicaloperational data associated with the manufacturing facility. Thehistorical operational data includes information related to staffingconditions of the manufacturing facility over a previous period of time.The historical operational data may, for example, include a total numberof employees of the manufacturing facility, a total number of employeesof the manufacturing facility who are temporary employees, a total costto employ the employees of the manufacturing facility, an amount ofgoods created by the employees of the manufacturing facility, a totalnumber of hours worked by the employees of the manufacturing facility,and a total number of overtime hours worked by the employees of themanufacturing facility.

At operation 315, the risk modeling engine 220 identifies correlationsbetween the deviation report data and the historical operational data.Specifically, the risk modeling engine 220 identifies correlationsbetween occurrences of deviations and staffing conditions. In someinstances, the risk modeling engine 220 may identify a series ofnon-linear exponential correlated risk factors. The identification ofthe correlations between the deviation report data and the historicaloperational data results in the identification of factors thatcontribute to risk in the manufacturing facility, and the degree towhich each factor contributes to the risk.

At operation 320, the risk modeling engine 220 generates a risk datamodel corresponding to the manufacturing facility using the identifiedcorrelations. In particular, the risk data model includes one or morerisk metrics (e.g., factors contributing to risk) corresponding to theidentified correlations. As an example, FIG. 4 is a block diagramillustrating an example risk data model 400, consistent with someembodiments. As shown, the risk data model 400 includes risk metrics401-40 n, each of which is a factor that contributes to the overall riskof the manufacturing facility. In some embodiments, the risk metrics401-40 n correspond to correlations between deviation report data andoperational data associated with the manufacturing facility. The riskmetrics 401-40 n may, in some embodiments, also relate to financialrisks (e.g., risk of financial issues affecting the manufacturing site),manufacturing risk (e.g., risk of manufacturing problems such as thosecaused by legacy systems), risks associated with excipients (e.g., riskof input materials and chemicals causing a problem), risks associatedwith change control (e.g., risk associated with change control eventssuch as new processes or procedures, or failure to adhere to existingones), and risk associated with mother nature (e.g., lightning storms,high humidity, high or low temperatures, or the like), for example. Asshown, the risk data model 400 also includes weights 411-41 n, each ofwhich corresponds to a respective risk metric 401-40 n, and indicates adegree to which the respective risk metric 401-40 n contributes to theoverall risk of the manufacturing facility.

Referring back to FIG. 3, at operation 325, the risk modeling engine 220stores the risk data model in the database 116 for subsequent retrieval.The risk data model may be indexed according to, or otherwise associatedwith, an identifier of the manufacturing facility such that the riskdata model may be subsequently retrieved for use in calculating riskscores specifically associated with the manufacturing facility.

FIG. 5 is a flowchart illustrating a method 500 for determining riskassociated with a manufacturing facility, according to some embodiments.The method 500 may be embodied in computer-readable instructions forexecution by one or more processors such that the operations of themethod 500 may be performed in part or in whole by the applicationserver 112. In particular, the operations of the method 500 may beperformed in part or in whole by the manufacturing risk analyticsservice 114; accordingly, the method 500 is described below by way ofexample with reference thereto. However, it shall be appreciated that atleast some of the operations of the method 500 may be deployed onvarious other hardware configurations and the method 500 is not intendedto be limited to the application server 112 or the manufacturing riskanalytics service 114.

At operation 505, the interface module 200 obtains operational dataassociated with a manufacturing facility. The operational data includesinformation related to staffing conditions of the manufacturingfacility. The operational data may relate to current or projectedstaffing conditions. The interface module 200 may receive theoperational data from a user via one or more user interfaces, or thedata retrieval module 205 may provision the interface module 200 withthe operational data after retrieving it from one or more third partysources.

At operation 510, the risk scoring module 225 accesses a risk data modelcorresponding to the manufacturing facility (e.g., from the database116). The risk data model includes risk metrics that contribute to theoverall risk of the manufacturing facility.

At operation 515, the risk scoring module 225 calculates, using the riskdata model, a risk score for the manufacturing facility based on theoperational data received at operation 505. As an example, FIG. 6 is aflowchart illustrating a method 600 for calculating a risk scoreassociated with a manufacturing facility, according to some embodiments.Consistent with some embodiments, the method 600 may correspond tooperation 515 of the method 500.

The method 600 may be embodied in computer-readable instructions forexecution by one or more processors such that the operations of themethod 600 may be performed in part or in whole by the applicationserver 112. In particular, the operations of the method 600 may beperformed in part or in whole by the functional components of themanufacturing risk analytics service 114; accordingly, the method 600 isdescribed below by way of example with reference thereto. However, itshall be appreciated that at least some of the operations of the method600 may be deployed on various other hardware configurations and themethod 600 is not intended to be limited to the application server 112.

At operation 605, the risk scoring module 225 determines risk metricvalues for each of the risk metrics included in the risk data model ofthe manufacturing facility based on the operational data. At operation610, the risk scoring module 225 weights each risk metric valueaccording to the respective weight of each risk metric specified by therisk data model. At operation 615, the risk scoring module 225aggregates the weighted risk metric values to generate the risk scorefor the manufacturing facility. For example, the risk scoring module 225may calculate the average of the weighted risk metric values todetermine the overall risk score for the manufacturing facility.

Referring back to FIG. 5, at operation 520, the risk scoring module 225determines a risk level of the manufacturing facility based on the riskscore. The risk scoring module 225 determines the risk level based onthe range of risk scores the risk score determined in operation 515falls into. The risk level may, for example, be high, medium, or low.

At operation 525, which is optional in some embodiments, the riskscoring module 225 accesses production target data including aproduction target (e.g., an anticipated recovery). The production targetdata may be included in the operational data associated with themanufacturing facility or it may be received directly from a user via auser interface provided by the interface module 200.

At operation 530, which is optional in some embodiments, the riskscoring module 225 determines a risk level associated with theproduction target using the risk data model and the determined riskmetric values. The risk scoring module 225 may determine the risk levelassociated with the production target based on a comparison of riskscores associated with the manufacturing facility at previous production(e.g., recovery) levels.

At operation 535, the interface module 200 causes presentation of a userinterface on the client device 102. The user interface includes the riskscore and the risk level associated with the manufacturing facilityalong with other information such as the risk level associated with aproduction target. As an example of the foregoing user interface, FIG. 7is an interface diagram illustrating a user interface 700 for providingan overview of the risk associated with a manufacturing facility,according to example embodiments.

As shown, the user interface 700 includes a graph 702 that illustratesrisk scores associated with a manufacturing facility over a period oftime, which in this example relate to deviations occurring over theperiod of time. The graph 702 includes the risk scores for both aprevious and a current year. It shall be appreciated that a year issimply an example time period over which risk scores may be graphed,although, in other embodiments, a user may specific other time periodfor the graph 702.

In some instances, the method 500 may initially be performed for currentoperational data and then repeated for projected operational data toproduce a current risk score and a projected risk score. That is, a riskscore is initially calculated based on the current operational data, andthen the risk score is recalculated based on the projected operationaldata. An example of the current and projected risk scores is included inthe user interface 700. In particular, a window 704 includes a currentrisk score, and a window 706 includes a projected risk score for thenext month.

Further examples of interfaces provided by the interface module 200 arediscussed below in reference to FIGS. 8-16. FIG. 8, for example, is aninterface diagram illustrating a user interface 800 for providing anoverview of the risk associated with a manufacturing facility, accordingto an alternative example. As shown, the user interface 800 includes anumber of risk analytics associated with the manufacturing facility. Therisk analytics represented in the user interface 800 are developed bythe data analysis module 210 through an analysis of deviation reportdata and operational data associated with the manufacturing facility.

In particular, the user interface 800 includes risk analytics associatedwith deviations occurring at the manufacturing facility, such as weeklydeviations 802, repeat deviations 804 (e.g., multiple instances of thesame deviation occurring within a predefined time range), and past duedeviations 806 (e.g., deviations awaiting corrective action). The userinterface 800 also includes risk analytics associated with tasksperformed at the manufacturing facility, such as overdue tasks 808,tasks performed 810, and percent of tasks overdue 812. The userinterface 800 also includes risk analytics associated with manufacturingaudits, such as customer complaints 814, audits underway 816, andupcoming audits 818.

FIG. 9 is an interface diagram illustrating a user interface 900 forproviding an overview of deviations occurring at a manufacturingfacility, according to example embodiments. The information presented inthe user interface 900 is developed by the data analysis module 210based on an analysis of deviation report data and operational dataassociated with the manufacturing facility. As shown, the user interface900 includes a graph 902 that illustrates deviations that have occurredat the manufacturing facility over a period of time. The user interface900 also includes a window 904 displaying a total count of currentdeviations, and a window 906 displaying a year-to-date count ofdeviations. The user interface 900 also includes a table 908 showing acount of deviations occurring by type and location. The location of adeviation refers to the location within the manufacturing facility wherethe deviation occurred (e.g., a particular product line or work center).

FIG. 10 is an interface diagram illustrating a user interface 1000 forproviding a detailed breakdown of deviations occurring at amanufacturing facility, according to example embodiments. Theinformation presented in the user interface 1000 is developed by thedata analysis module 210 based on an analysis of deviation report dataand operational data associated with the manufacturing facility. Asshown, the user interface 1000 includes multiple charts displaying anumber of deviations occurring in the manufacturing facility by subtype1002, root cause 1004, work center 1006, line-operation 1008, product1010, and criticality 1020.

FIG. 11 is an interface diagram illustrating a user interface 1100 forpresenting information related to chronic deviations in a manufacturingfacility, according to example embodiments. The information presented inthe user interface 1100 is developed by the data analysis module 210based on an analysis of deviation report data and operational dataassociated with the manufacturing facility. As shown, the user interface1100 includes a graph 1102 illustrating deviations occurring at themanufacturing facility over a period of time, along with repeatdeviations occurring at the manufacturing facility over the same periodof time. The user interface 1100 also includes a window 1104 displayingthe total number of deviations over the period of time, and a window1106 displaying the percentage of the deviations that are repeatdeviations. The user interface 1100 also includes a chart 1108displaying a breakdown of repeat deviations by type. The chart 1108includes a display of the number of each type of deviation that arerepeat deviations. The chart 1108 also includes indicators (e.g.,colors) of criticality (e.g., minor, major, critical) for each repeatdeviation.

FIG. 12 is an interface diagram illustrating a user interface 1200 forpresenting deviation timelines, according to example embodiments. Theinformation presented in the user interface 1200 is developed by thedata analysis module 210 based on an analysis of deviation report dataand operational data associated with a manufacturing facility. As shown,the user interface 1200 includes timelines 1201-1208, each of whichcorresponds to a repeat deviation. The user interface 1200 alsoindicates a line 1210 (e.g., manufacturing procedure) from which eachrepeat deviation originated and a root cause 1212 corresponding to eachof the timelines 1201-1208. Each of the timelines 1201-1208 furtherincludes an indicator (e.g., a rectangle) of remediation actions takento remedy the deviations along with an indication of the status (e.g.,complete or incomplete) of the remediation action. For example,rectangles are used in the user interface 1200 to denote remediationactions, and the color of each rectangle is used to denote the status ofthe corresponding remediation action. Further, the duration of each ofthe remediation actions is denoted by the length of the correspondingrectangle. Each of the timelines 1201-1208 also includes an indicator(e.g., colored circle) of criticality (e.g., minor, major, critical) forthe corresponding repeat deviation.

FIG. 13 is an interface diagram illustrating a user interface 1300 forpresenting risk analytics related to staffing conditions, according toexample embodiments. As shown, the user interface 1300 includesstaffing-related risk analytics over a specified time period. A user mayspecify the time period of analysis using date drop-down menus 1302. Asshown, the user interface 1300 includes a window 1304 for displaying acurrent risk level (e.g., high, medium, or low) and a window 1306 fordisplaying a projected risk level, which in this example corresponds tothe peak risk level for the next three months.

The risk levels displayed in the windows 1304 and 1306 are determined bythe risk scoring module 225 based on an analysis of operational data(historical, current, and projected) in light of various risk metricsincluded in a risk data model corresponding to a manufacturing facility.In this example, the risk metrics relate to staffing conditions of themanufacturing facility. In particular, in this example, the risk metricsinclude headcount change, productivity, percent overtime, and percenttemporary employees. As shown, the user interface 1300 includes charts1308-1311, each of which corresponds to a risk metric included in therisk data model. In particular, each of the charts 1308-1311 includeshistorical, current, and projected risk metric values for thecorresponding risk metric over the period of time specified by the datedrop-down menus 1302. The risk metric values are determined by the riskscoring module 225 based on analysis of the manufacturing facility'soperational data. Each of the charts 1308-1311 also includes anindicator of the risk level associated with the corresponding riskmetric value, which is denoted by color. The risk level of each riskmetric value corresponds to the weighted risk metric value determinedduring the process of calculating a risk score associated with themanufacturing facility.

As an example, the chart 1308 includes historical, current, andprojected values for headcount change, which represents the change tothe total number of employees of the manufacturing facility, over theperiod of time specified by the date drop-down menus 1302. The chart1309 includes historical, current, and projected values forproductivity, which represents an amount of goods created at themanufacturing facility relative to the amount of people creating thegoods, over the period of time specified by the date drop-down menus1302. The chart 1310 includes historical, current, and projected valuesfor percent overtime, which represents an amount of overtime hoursworked at the manufacturing facility relative to the total number ofhours worked at the manufacturing facility, over the period of timespecified by the date drop-down menus 1302. The chart 1311 includeshistorical, current, and projected values for percent temporaryemployees, which represents a number of temporary employees of themanufacturing facility relative to the total number of employees in themanufacturing facility workforce, over the period of time specified bythe date drop-down menus 1302.

The user interface 1300 also includes a chart 1312 illustratinghistorical, current, and projected values for recovery of themanufacturing facility over the period of time specified by the datedrop-down menus 1302. The recovery of the manufacturing facilityrepresents an amount of goods being created (e.g., amount in dollars ofrevenue generated) by the manufacturing facility. The chart 1312 alsoincludes an indicator of risk level for each recovery value. The riskscoring module 225 determines the risk level associated with eachhistorical, current, and projected recovery value based on an analysisof the manufacturing facility's operational data in light of the riskdata model associated with the manufacturing facility.

FIG. 14 is an interface diagram illustrating a user interface 1400 forpresenting risk analytics associated with future projections, accordingto example embodiments. As shown, the user interface 1400 includesbuttons 1402 and 1404 for receiving user input indicative of projectedoperational data of a manufacturing facility. In particular, the buttons1402 and 1404 are used to specify projected operational data that mayhave an effect on one or more risk metrics included in a risk data modelcorresponding to the manufacturing facility. More specifically, in thisexample, the button 1402 may be used to input a projected value for anumber of temporary employees who will be converted into full-timeemployees, and the button 1404 may be used to input a projected valuefor a number of new hires who are temporary employees.

Inputs received at either of the buttons 1402 and 1404 may change one ormore risk metric values, and thus, change not only the projected overallrisk score, but the individual risk levels associated with each riskmetric as well. Accordingly, upon receiving inputs at either one of thebuttons 1402 or 1404 indicative of projected operational data, the riskscoring module 225 recalculates risk levels for at least a portion ofthe risk metrics in the risk data model, and the recalculated risklevels are displayed in the user interface 1400. In particular, the riskscoring module 225 recalculates risk levels for a set of risk metricpairs (e.g., overtime and headcount) in light of the input received viathe buttons 1402 and 1404. The risk level for each risk metric pair isdisplayed in the user interface 1400 in a different block so as toillustrate the relationship of each risk metric pair. For example, ablock 1406 represents a +5% change in overtime hours and a −5% change inheadcount. Similarly, a block 1408 represents a −7% change in overtimehours and a +5% change in headcount. In the user interface 1400, therisk level of each block is denoted by color. It shall be appreciatedthat although FIG. 14 illustrates only change in overtime hours andchange in headcount, the user interface 1400 is not limited to theserisk metrics, and other risk metrics may be used in other embodiments.

FIG. 15 is an interface diagram illustrating a user interface 1500 forpresenting an overview of tasks associated with a manufacturingfacility, according to example embodiments. The information presented inthe user interface 1500 is developed by the data analysis module 210based on an analysis of task data included as part of operational dataassociated with the manufacturing facility. As shown, the user interface1500 includes a drop-down menu 1502 that allows a user to input a datefor inspection. The user interface 1500 includes a window 1504 thatdisplays a number of overdue tasks, a window 1506 that displays a numberof tasks that are overdue beyond a threshold period of time (e.g., past30 days), a window 1508 that displays a number of tasks due during theperiod of time, and a window 1510 that displays a number of tasks dueduring an upcoming period of time.

The user interface 1500 further includes a graph 1512 that displaysoverdue tasks over the period of time. The user interface 1500 furtherincludes a graph 1514 that displays planned and upcoming tasks over theperiod of time.

FIG. 16 is an interface diagram illustrating a user interface 1600 forpresenting a detailed breakdown of tasks associated with a manufacturingfacility, according to example embodiments. The information presented inthe user interface 1600 is developed by the data analysis module 210based on an analysis of task data included as part of operational dataassociated with the manufacturing facility. As shown, the user interface1600 includes charts 1602-1606 for displaying task-related information.In particular, the chart 1602 shows number of tasks by owner (e.g., anemployee assigned to complete the task), the chart 1603 shows number oftasks by days overdue, the chart 1604 shows number of tasks by due date,the chart 1605 shows number of tasks by status, and the chart 1606 showsnumber of tasks by criticality. The charts 1602, 1605, and 1606 furtherinclude indicators of task status (e.g., overdue for more than 30 days,overdue, scheduled, and upcoming) as denoted by color.

Modules, Components, and Logic

Certain embodiments are described herein as including logic or a numberof components, modules, or mechanisms. Modules may constitute eithersoftware modules (e.g., code embodied on a machine-readable medium) orhardware modules. A “hardware module” is a tangible unit capable ofperforming certain operations and may be configured or arranged in acertain physical manner. In various example embodiments, one or morecomputer systems (e.g., a standalone computer system, a client computersystem, or a server computer system) or one or more hardware modules ofa computer system (e.g., a processor or a group of processors) may beconfigured by software (e.g., an application or application portion) asa hardware module that operates to perform certain operations asdescribed herein.

In some embodiments, a hardware module may be implemented mechanically,electronically, or any suitable combination thereof. For example, ahardware module may include dedicated circuitry or logic that ispermanently configured to perform certain operations. For example, ahardware module may be a special-purpose processor, such as aField-Programmable Gate Array (FPGA) or an Application SpecificIntegrated Circuit (ASIC). A hardware module may also includeprogrammable logic or circuitry that is temporarily configured bysoftware to perform certain operations. For example, a hardware modulemay include software executed by a general-purpose processor or otherprogrammable processor. Once configured by such software, hardwaremodules become specific machines (or specific components of a machine)uniquely tailored to perform the configured functions and are no longergeneral-purpose processors. It will be appreciated that the decision toimplement a hardware module mechanically, in dedicated and permanentlyconfigured circuitry, or in temporarily configured circuitry (e.g.,configured by software) may be driven by cost and time considerations.

Accordingly, the phrase “hardware module” should be understood toencompass a tangible entity, be that an entity that is physicallyconstructed, permanently configured (e.g., hardwired), or temporarilyconfigured (e.g., programmed) to operate in a certain manner or toperform certain operations described herein. As used herein,“hardware-implemented module” refers to a hardware module. Consideringembodiments in which hardware modules are temporarily configured (e.g.,programmed), each of the hardware modules need not be configured orinstantiated at any one instance in time. For example, where a hardwaremodule comprises a general-purpose processor configured by software tobecome a special-purpose processor, the general-purpose processor may beconfigured as respectively different special-purpose processors (e.g.,comprising different hardware modules) at different times. Softwareaccordingly configures a particular processor or processors, forexample, to constitute a particular hardware module at one instance oftime and to constitute a different hardware module at a differentinstance of time.

Hardware modules can provide information to, and receive informationfrom, other hardware modules. Accordingly, the described hardwaremodules may be regarded as being communicatively coupled. Where multiplehardware modules exist contemporaneously, communications may be achievedthrough signal transmission (e.g., over appropriate circuits and buses)between or among two or more of the hardware modules. In embodiments inwhich multiple hardware modules are configured or instantiated atdifferent times, communications between such hardware modules may beachieved, for example, through the storage and retrieval of informationin memory structures to which the multiple hardware modules have access.For example, one hardware module may perform an operation and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware module may then, at a latertime, access the memory device to retrieve and process the storedoutput. Hardware modules may also initiate communications with input oroutput devices, and can operate on a resource (e.g., a collection ofinformation).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented modulesthat operate to perform one or more operations or functions describedherein. As used herein, “processor-implemented module” refers to ahardware module implemented using one or more processors.

Similarly, the methods described herein may be at least partiallyprocessor-implemented, with a particular processor or processors beingan example of hardware. For example, at least some of the operations ofa method may be performed by one or more processors orprocessor-implemented modules.

Moreover, the one or more processors may also operate to supportperformance of the relevant operations in a “cloud computing”environment or as a “software as a service” (SaaS). For example, atleast some of the operations may be performed by a group of computers(as examples of machines including processors), with these operationsbeing accessible via a network (e.g., the Internet) and via one or moreappropriate interfaces (e.g., an API).

The performance of certain of the operations may be distributed amongthe processors, not only residing within a single machine, but deployedacross a number of machines. In some example embodiments, the processorsor processor-implemented modules may be located in a single geographiclocation (e.g., within a home environment, an office environment, or aserver farm). In other example embodiments, the processors orprocessor-implemented modules may be distributed across a number ofgeographic locations.

Example Machine Architecture and Machine-Readable

FIG. 17 is a block diagram illustrating components of a machine 1700,according to some example embodiments, able to read instructions from amachine-readable medium (e.g., a machine-readable storage medium) andperform any one or more of the methodologies discussed herein.Specifically, FIG. 17 shows a diagrammatic representation of the machine1700 in the example form of a computer system, within which instructions1716 (e.g., software, a program, an application, an applet, an app, orother executable code) for causing the machine 1700 to perform any oneor more of the methodologies discussed herein may be executed.Additionally, or alternatively, the machine 1700 may correspond to anyone of the client device 102, the web server 110, the application server112, or the third-party computing system 118. The instructions transformthe general, non-programmed machine into a particular machine programmedto carry out the described and illustrated functions in the mannerdescribed. In alternative embodiments, the machine 1700 operates as astandalone device or may be coupled (e.g., networked) to other machines.In a networked deployment, the machine 1700 may operate in the capacityof a server machine or a client machine in a server-client networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine 1700 may comprise, but not be limitedto, a server computer, a client computer, a personal computer (PC), atablet computer, a laptop computer, a netbook, a set-top box (STB), aPDA, an entertainment media system, a cellular telephone, a smart phone,a mobile device, a wearable device (e.g., a smart watch), a smart homedevice (e.g., a smart appliance), other smart devices, a web appliance,a network router, a network switch, a network bridge, or any machinecapable of executing the instructions 1716, sequentially or otherwise,that specify actions to be taken by the machine 1700. Further, whileonly a single machine 1700 is illustrated, the term “machine” shall alsobe taken to include a collection of machines 1700 that individually orjointly execute the instructions 1716 to perform any one or more of themethodologies discussed herein.

The machine 1700 may include processors 1710, memory/storage 1730, andI/O components 1750, which may be configured to communicate with eachother such as via a bus 1702. In an example embodiment, the processors1710 (e.g., a Central Processing Unit (CPU), a Reduced Instruction SetComputing (RISC) processor, a Complex Instruction Set Computing (CISC)processor, a Graphics Processing Unit (GPU), a Digital Signal Processor(DSP), an ASIC, a Radio-Frequency Integrated Circuit (RFIC), anotherprocessor, or any suitable combination thereof) may include, forexample, a processor 1712 and a processor 1714 that may execute theinstructions 1716. The term “processor” is intended to includemulti-core processor that may comprise two or more independentprocessors (sometimes referred to as “cores”) that may executeinstructions contemporaneously. Although FIG. 17 shows multipleprocessors, the machine 1700 may include a single processor with asingle core, a single processor with multiple cores (e.g., a multi-coreprocessor), multiple processors with a single core, multiple processorswith multiples cores, or any combination thereof.

The memory/storage 1730 may include a memory 1732, such as a mainmemory, or other memory storage, and a storage unit 1736, bothaccessible to the processors 1710 such as via the bus 1702. The storageunit 1736 and memory 1732 store the instructions 1716 embodying any oneor more of the methodologies or functions described herein. Theinstructions 1716 may also reside, completely or partially, within thememory 1732, within the storage unit 1736, within at least one of theprocessors 1710 (e.g., within the processor's cache memory), or anysuitable combination thereof, during execution thereof by the machine1700. Accordingly, the memory 1732, the storage unit 1736, and thememory of the processors 1710 are examples of machine-readable media.

As used herein, “machine-readable medium” means a device able to storeinstructions and data temporarily or permanently, and may include, butis not limited to, random-access memory (RAM), read-only memory (ROM),buffer memory, flash memory, optical media, magnetic media, cachememory, other types of storage (e.g., Erasable Programmable Read-OnlyMemory (EEPROM)), and/or any suitable combination thereof. The term“machine-readable medium” should be taken to include a single medium ormultiple media (e.g., a centralized or distributed database, orassociated caches and servers) able to store the instructions 1716. Theterm “machine-readable medium” shall also be taken to include anymedium, or combination of multiple media, that is capable of storinginstructions (e.g., instructions 1716) for execution by a machine (e.g.,machine 1700), such that the instructions, when executed by one or moreprocessors of the machine (e.g., processors 1710), cause the machine toperform any one or more of the methodologies described herein.Accordingly, a “machine-readable medium” refers to a single storageapparatus or device, as well as “cloud-based” storage systems or storagenetworks that include multiple storage apparatus or devices. The term“machine-readable medium” excludes signals per se.

Furthermore, the machine-readable medium is non-transitory in that itdoes not embody a propagating signal. However, labeling the tangiblemachine-readable medium “non-transitory” should not be construed to meanthat the medium is incapable of movement—the medium should be consideredas being transportable from one real-world location to another.Additionally, since the machine-readable medium is tangible, the mediummay be considered to be a machine-readable device.

The I/O components 1750 may include a wide variety of components toreceive input, provide output, produce output, transmit information,exchange information, capture measurements, and so on. The specific I/Ocomponents 1750 that are included in a particular machine will depend onthe type of machine. For example, portable machines such as mobilephones will likely include a touch input device or other such inputmechanisms, while a headless server machine will likely not include sucha touch input device. It will be appreciated that the I/O components1750 may include many other components that are not shown in FIG. 17.The I/O components 1750 are grouped according to functionality merelyfor simplifying the following discussion and the grouping is in no waylimiting. In various example embodiments, the I/O components 1750 mayinclude output components 1752 and input components 1754. The outputcomponents 1752 may include visual components (e.g., a display such as aplasma display panel (PDP), a light emitting diode (LED) display, aliquid crystal display (LCD), a projector, or a cathode ray tube (CRT)),acoustic components (e.g., speakers), haptic components (e.g., avibratory motor, resistance mechanisms), other signal generators, and soforth. The input components 1754 may include alphanumeric inputcomponents (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or another pointinginstrument), tactile input components (e.g., a physical button, a touchscreen that provides location and/or force of touches or touch gestures,or other tactile input components), audio input components (e.g., amicrophone), and the like.

In further example embodiments, the I/O components 1750 may includebiometric components 1756, motion components 1758, environmentalcomponents 1760, or position components 1762 among a wide array of othercomponents. For example, the biometric components 1756 may includecomponents to detect expressions (e.g., hand expressions, facialexpressions, vocal expressions, body gestures, or eye tracking), measurebiosignals (e.g., blood pressure, heart rate, body temperature,perspiration, or brain waves), identify a person (e.g., voiceidentification, retinal identification, facial identification,fingerprint identification, or electroencephalogram basedidentification), and the like. The motion components 1758 may includeacceleration sensor components (e.g., accelerometer), gravitation sensorcomponents, rotation sensor components (e.g., gyroscope), and so forth.The environmental components 1760 may include, for example, illuminationsensor components (e.g., photometer), temperature sensor components(e.g., one or more thermometers that detect ambient temperature),humidity sensor components, pressure sensor components (e.g.,barometer), acoustic sensor components (e.g., one or more microphonesthat detect background noise), proximity sensor components (e.g.,infrared sensors that detect nearby objects), gas sensors (e.g., gasdetection sensors to detect concentrations of hazardous gases for safetyor to measure pollutants in the atmosphere), or other components thatmay provide indications, measurements, or signals corresponding to asurrounding physical environment. The position components 1762 mayinclude location sensor components (e.g., a Global Position System (GPS)receiver component), altitude sensor components (e.g., altimeters orbarometers that detect air pressure from which altitude may be derived),orientation sensor components (e.g., magnetometers), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 1750 may include communication components 1764operable to couple the machine 1700 to a network 1780 or devices 1770via a coupling 1782 and a coupling 1772, respectively. For example, thecommunication components 1764 may include a network interface componentor other suitable device to interface with the network 1780. In furtherexamples, the communication components 1764 may include wiredcommunication components, wireless communication components, cellularcommunication components, Near Field Communication (NFC) components,Bluetooth® components (e.g., Bluetooth® Low Energy), Wi-Fi® components,and other communication components to provide communication via othermodalities. The devices 1770 may be another machine or any of a widevariety of peripheral devices (e.g., a peripheral device coupled via aUniversal Serial Bus (USB)).

Moreover, the communication components 1764 may detect identifiers orinclude components operable to detect identifiers. For example, thecommunication components 1764 may include Radio Frequency Identification(RFID) tag reader components, NFC smart tag detection components,optical reader components (e.g., an optical sensor to detectone-dimensional bar codes such as Universal Product Code (UPC) bar code,multi-dimensional bar codes such as Quick Response (QR) code, Azteccode, Data Matrix, Dataglyph, MaxiCode, PDF4117, Ultra Code, UCC RSS-2Dbar code, and other optical codes), or acoustic detection components(e.g., microphones to identify tagged audio signals). In addition, avariety of information may be derived via the communication components1764, such as location via Internet Protocol (IP) geo-location, locationvia Wi-Fi® signal triangulation, location via detecting an NFC beaconsignal that may indicate a particular location, and so forth.

Transmission Medium

In various example embodiments, one or more portions of the network 1780may be an ad hoc network, an intranet, an extranet, a VPN, a LAN, aWLAN, a WAN, a WWAN, a MAN, the Internet, a portion of the Internet, aportion of the PSTN, a plain old telephone service (POTS) network, acellular telephone network, a wireless network, a Wi-Fi® network,another type of network, or a combination of two or more such networks.For example, the network 1780 or a portion of the network 1780 mayinclude a wireless or cellular network and the coupling 1782 may be aCode Division Multiple Access (CDMA) connection, a Global System forMobile communications (GSM) connection, or another type of cellular orwireless coupling. In this example, the coupling 1782 may implement anyof a variety of types of data transfer technology, such as SingleCarrier Radio Transmission Technology (1×RTT), Evolution-Data Optimized(EVDO) technology, General Packet Radio Service (GPRS) technology,Enhanced Data rates for GSM Evolution (EDGE) technology, thirdGeneration Partnership Project (3GPP) including 3G, fourth generationwireless (4G) networks, Universal Mobile Telecommunications System(UMTS), High Speed Packet Access (HSPA), Worldwide Interoperability forMicrowave Access (WiMAX), Long Term Evolution (LTE) standard, othersdefined by various standard-setting organizations, other long rangeprotocols, or other data transfer technology.

The instructions 1716 may be transmitted or received over the network1780 using a transmission medium via a network interface device (e.g., anetwork interface component included in the communication components1764) and using any one of a number of well-known transfer protocols(e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions1716 may be transmitted or received using a transmission medium via thecoupling 1772 (e.g., a peer-to-peer coupling) to the devices 1770. Theterm “transmission medium” shall be taken to include any intangiblemedium that is capable of storing, encoding, or carrying theinstructions 1716 for execution by the machine 1700, and includesdigital or analog communications signals or other intangible media tofacilitate communication of such software.

Language

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Although an overview of the inventive subject matter has been describedwith reference to specific example embodiments, various modificationsand changes may be made to these embodiments without departing from thebroader scope of embodiments of the present disclosure. Such embodimentsof the inventive subject matter may be referred to herein, individuallyor collectively, by the term “invention” merely for convenience andwithout intending to voluntarily limit the scope of this application toany single disclosure or inventive concept if more than one is, in fact,disclosed.

The embodiments illustrated herein are described in sufficient detail toenable those skilled in the art to practice the teachings disclosed.Other embodiments may be used and derived therefrom, such thatstructural and logical substitutions and changes may be made withoutdeparting from the scope of this disclosure. The Detailed Description,therefore, is not to be taken in a limiting sense, and the scope ofvarious embodiments is defined only by the appended claims, along withthe full range of equivalents to which such claims are entitled.

As used herein, the term “or” may be construed in either an inclusive orexclusive sense. Moreover, plural instances may be provided forresources, operations, or structures described herein as a singleinstance. Additionally, boundaries between various resources,operations, modules, engines, and data stores are somewhat arbitrary,and particular operations are illustrated in a context of specificillustrative configurations. Other allocations of functionality areenvisioned and may fall within a scope of various embodiments of thepresent disclosure. In general, structures and functionality presentedas separate resources in the example configurations may be implementedas a combined structure or resource. Similarly, structures andfunctionality presented as a single resource may be implemented asseparate resources. These and other variations, modifications,additions, and improvements fall within a scope of embodiments of thepresent disclosure as represented by the appended claims. Thespecification and drawings are, accordingly, to be regarded in anillustrative rather than a restrictive sense.

In this document, the terms “a” or “an” are used, as is common in patentdocuments, to include one or more than one, independent of any otherinstances or usages of “at least one” or “one or more.” In the appendedclaims, the terms “including” and “in which” are used as theplain-English equivalents of the respective terms “comprising” and“wherein.” Also, in the following claims, the terms “including” and“comprising” are open-ended; that is, a system, device, article, orprocess that includes elements in addition to those listed after such aterm in a claim are still deemed to fall within the scope of that claim.Moreover, in the following claims, the terms “first,” “second,” “third,”and so forth are used merely as labels, and are not intended to imposenumerical requirements on their objects.

What is claimed is:
 1. A method comprising: performing an analysis of historical staffing conditions of a manufacturing facility and one or more deviations from existing manufacturing procedures described in one or more deviation reports, the performing of the analysis including analyzing textual descriptions of the one or more deviations from existing manufacturing procedures included in the one or more deviation reports, the historical staffing conditions including historical information related to employees of the manufacturing facility; generating a risk data model for the manufacturing facility based at least in part on one or more correlations identified based on the analysis, the risk data model including a plurality of risk metrics related to the identified correlations; receiving, from a client device, projected operational data associated with the manufacturing facility, the projected operational data including information related to anticipated future staffing conditions of the manufacturing facility; calculating, using the risk data model, a risk score based on the projected operational data, the risk score providing a measure of risk associated with the anticipated future staffing conditions of the manufacturing facility, the calculating of the risk score being performed by one or more processors of a machine; and causing presentation of a user interface on the client device, the user interface including a display of the risk score.
 2. The method of claim 1, wherein: the risk score is an anticipated risk score; the method further comprises calculating, using the risk data model, a current risk score based on current operational data, the current operational data including current staffing conditions of the manufacturing facility, the current risk score providing a measure of risk associated with the current staffing conditions of the manufacturing facility, the calculating of the risk score being performed by one or more processors of a machine; and the user interface further includes a display of the current risk score.
 3. The method of claim 1, wherein the user interface further includes a graph of one or more previous risk scores of the manufacturing facility.
 4. The method of claim 1, wherein the user interface further includes a breakdown of the one of more deviations from existing manufacturing procedures by at least one of type, subtype, root cause, location, product, or criticality.
 5. The method of claim 1, wherein the generating of the risk data model comprises: obtaining deviation data associated with the manufacturing facility, the deviation data including the one or more deviation reports, each of the one or more deviation reports including a textual description of a deviation from existing manufacturing procedures; and obtaining historical operational data associated with the manufacturing facility, the historical operational data comprising the historical staffing conditions.
 6. The method of claim 1, wherein the historical staffing conditions include at least one of a total number of employees of the manufacturing facility, a total number of temporary employees of the manufacturing facility, a total cost corresponding to the employees of the manufacturing facility, an amount of goods created by the employees of the manufacturing facility, a total number of hours worked by the employees of the manufacturing facility, or a total number of overtime hours worked by the employees of the manufacturing facility.
 7. The method of claim 1, further comprising determining a deviation type of each of the one or more deviations from existing manufacturing procedures based on the analysis of the textual description included in each of the one or more deviation reports; the user interface further including a breakdown of the one or more deviations by deviation type.
 8. The method of claim 1, wherein the calculating of the risk score comprises: determining a risk metric value for each risk metric of the plurality of risk metrics based on the operational data; applying a weight to each of the risk metric values based on the risk data model to generate weighted risk metric values; and calculating an average of the weighted risk metric values; wherein the risk score includes the average of the weighted risk metric values.
 9. The method of claim 8, wherein the plurality of risk metrics includes a headcount change, a productivity measure, overtime hours as a percentage of all worked hours, and a percentage of employees who are temporary employees.
 10. The method of claim 1, further comprising: receiving production target data including a production target of the manufacturing facility; and determining a risk level corresponding to the production target.
 11. The method of claim 1, further comprising identifying a repeat deviation based on analysis of the one or more deviations reports, the repeat deviation corresponding to multiple occurrences of an identical deviation occurring within a predefined time range.
 12. The method of claim 1, further comprising determining a risk level of the manufacturing facility based on the risk score, wherein the user interface includes a display of the risk level.
 13. A system comprising: one or more processors; and a memory storing instructions that, when executed by the one or more processors, cause the system to perform operations comprising: performing an analysis of historical staffing conditions of a manufacturing facility and one or more deviations from existing manufacturing procedures described in one or more deviation reports, the performing of the analysis including analyzing textual descriptions of the one or more deviations from existing manufacturing procedures included in each of the one or more deviation reports, the historical staffing conditions including historical information related to employees of the manufacturing facility; generating a risk data model for the manufacturing facility based at least in part on one or more correlations identified based on the analysis, the risk data model including a plurality of risk metrics related to the identified correlations; receiving, from a client device, projected operational data associated with the manufacturing facility, the projected operational data including information related to anticipated future staffing conditions of the manufacturing facility; calculating, using the risk data model, an risk score based on the projected operational data, the risk score providing a measure of risk associated with the anticipated future staffing conditions of the manufacturing facility, the calculating of the risk score being performed by one or more processors of a machine; and causing presentation of a user interface on the client device, the user interface including a display of the risk score.
 14. The system of claim 13, wherein: the risk score is an anticipated risk score; the operations further comprise, using the risk data model, a current risk score based on current operational data, the current operational data including current staffing conditions of the manufacturing facility, the current risk score providing a measure of risk associated with the current staffing conditions of the manufacturing facility, the calculating of the risk score being performed by one or more processors of a machine; and the user interface further includes a display of the current risk score.
 15. The system of claim 13, wherein the operations further comprise: obtaining deviation data associated with the manufacturing facility, the deviation data including the one or more deviation reports, each of the one or more deviation reports including a textual description of a deviation from existing manufacturing procedures; and obtaining historical operational data associated with the manufacturing facility, the historical operational data comprising the historical staffing conditions.
 16. The system of claim 13, wherein the operations further comprise: determining a risk metric value for each risk metric of the plurality of risk metrics based on the operational data; applying a weight to each of the risk metric values based on the risk data model to generate weighted risk metric values; and calculating an average of the weighted risk metric values to produce the risk score.
 17. The system of claim 16, wherein a risk level of each of the risk metrics based on the corresponding risk metric value, and wherein the user interface includes a display of the risk level of each of the risk metrics.
 18. The system of claim 13, wherein the operations further comprise identifying a repeat deviation based on analysis of the one or more deviations reports, the repeat deviation corresponding to multiple occurrences of an identical deviation occurring within a predefined time range.
 19. The system of claim 13, wherein the operations further comprise determining a deviation type of each of the one or more deviations from existing manufacturing procedures based on an analysis of the textual description included in each of the one or more deviation reports, the user interface further including a breakdown of the one or more deviations from existing manufacturing procedures by deviation type.
 20. A non-transitory machine-readable storage medium embodying instructions that, when executed by at least one processor of a machine, cause the machine to perform operations comprising: analyzing historical staffing conditions of a manufacturing facility and one or more deviations from existing manufacturing procedures described in one or more deviation reports, the performing of the analysis including analyzing textual descriptions of the one or more deviations from existing manufacturing procedures included in each of the one or more deviation reports, the historical staffing conditions including historical information related to employees of the manufacturing facility; generating a risk data model for the manufacturing facility based at least in part on one or more correlations identified based on the analysis, the risk data model including a plurality of risk metrics related to the identified correlations; receiving, from a client device, projected operational data associated with the manufacturing facility, the projected operational data including information related to anticipated future staffing conditions of the manufacturing facility; calculating, using the risk data model, a risk score based on the projected operational data, the risk score providing a measure of risk associated with the anticipated future staffing conditions of the manufacturing facility; and causing presentation of a user interface on the client device, the user interface including a display of the risk score. 